BACKGROUND: Sirolimus, an immunosuppressive agent used in renal transplantation, can prevent allograft rejection. Identification of the therapeutic index (the ratio of minimum toxic concentration to minimum therapeutic concentration) for immunosuppresants is necessary to optimize the care of patients and set standards for bioequivalence evaluation of sirolimus products. However, the therapeutic index for sirolimus has been inconsistently defined, potentially because of inconsistencies in sirolimus exposure-response relationships. METHODS: The authors used retrospective therapeutic drug monitoring data from the electronic health records of patients treated in a tertiary health care system from 2008 to 2014 to (1) develop a population pharmacokinetic (PK) model, (2) use the model to simulate sirolimus concentrations, and (3) characterize the exposure-response relationship. Using Wilcoxon rank-sum and Fisher exact tests, the authors determined relationships between sirolimus exposure and adverse events (AEs) (anemia, leukopenia, thrombocytopenia, hyperlipidemia, and decline in renal function) and the composite efficacy end point of graft loss or rejection. RESULTS: The developed 2-compartment population PK model showed appropriate goodness of fit. In a late-phase (>12 months), postrenal transplant population of 27 inpatients, the authors identified statistically significant relationships between 83 simulated peak and trough sirolimus concentrations and outcomes: graft loss or rejection (P = 0.018) and decline in renal function (P = 0.006), respectively. CONCLUSIONS: Use of therapeutic drug monitoring results and PK modeling permitted correlation of sirolimus concentrations with graft loss or rejection and decline in renal function. However, the method was limited in its assessment of other AEs. To better evaluate sirolimus exposure-response relationships, the method should be applied to a larger sample of newly transplanted patients with a higher propensity toward AEs or efficacy failure.
BACKGROUND: Sirolimus, an immunosuppressive agent used in renal transplantation, can prevent allograft rejection. Identification of the therapeutic index (the ratio of minimum toxic concentration to minimum therapeutic concentration) for immunosuppresants is necessary to optimize the care of patients and set standards for bioequivalence evaluation of sirolimus products. However, the therapeutic index for sirolimus has been inconsistently defined, potentially because of inconsistencies in sirolimus exposure-response relationships. METHODS: The authors used retrospective therapeutic drug monitoring data from the electronic health records of patients treated in a tertiary health care system from 2008 to 2014 to (1) develop a population pharmacokinetic (PK) model, (2) use the model to simulate sirolimus concentrations, and (3) characterize the exposure-response relationship. Using Wilcoxon rank-sum and Fisher exact tests, the authors determined relationships between sirolimus exposure and adverse events (AEs) (anemia, leukopenia, thrombocytopenia, hyperlipidemia, and decline in renal function) and the composite efficacy end point of graft loss or rejection. RESULTS: The developed 2-compartment population PK model showed appropriate goodness of fit. In a late-phase (>12 months), postrenal transplant population of 27 inpatients, the authors identified statistically significant relationships between 83 simulated peak and trough sirolimus concentrations and outcomes: graft loss or rejection (P = 0.018) and decline in renal function (P = 0.006), respectively. CONCLUSIONS: Use of therapeutic drug monitoring results and PK modeling permitted correlation of sirolimus concentrations with graft loss or rejection and decline in renal function. However, the method was limited in its assessment of other AEs. To better evaluate sirolimus exposure-response relationships, the method should be applied to a larger sample of newly transplanted patients with a higher propensity toward AEs or efficacy failure.
Authors: Ron J Keizer; Michel van Benten; Jos H Beijnen; Jan H M Schellens; Alwin D R Huitema Journal: Comput Methods Programs Biomed Date: 2010-06-02 Impact factor: 5.428
Authors: B D Kahan; M Welsh; L Schoenberg; L P Rutzky; S M Katz; D L Urbauer; C T Van Buren Journal: Transplantation Date: 1996-09-15 Impact factor: 4.939
Authors: Stefan M Weiner; Lorenz Sellin; Oliver Vonend; Peter Schenker; Nikolaus J Buchner; Markus Flecken; Richard Viebahn; Lars C Rump Journal: Nephrol Dial Transplant Date: 2007-07-04 Impact factor: 5.992
Authors: S Vitko; Z Wlodarczyk; L Kyllönen; Z Czajkowski; R Margreiter; L Backman; F Perner; P Rigotti; B Jaques; D Abramowicz; M Kessler; J Sanchez-Plumed; L Rostaing; R S Rodger; D Donati; Y Vanrenterghem Journal: Am J Transplant Date: 2006-03 Impact factor: 8.086
Authors: Shufan Ge; Ryan J Beechinor; Christoph P Hornik; Joseph F Standing; Kanecia Zimmerman; Michael Cohen-Wolkowiez; Matthew M Laughon; Reese Clark; Daniel Gonzalez Journal: Antimicrob Agents Chemother Date: 2018-08-27 Impact factor: 5.938